Association of inflammatory cytokines with lung function, chronic lung diseases, and COVID-19

Summary We investigated the effects of 35 inflammatory cytokines on respiratory outcomes, including COVID-19, asthma (atopic and non-atopic), chronic obstructive pulmonary disease (COPD), and pulmonary function indices, using Mendelian randomization and colocalization analyses. The emerging associations were further explored using observational analyses in the UK Biobank. We found an inverse association between genetically predicted macrophage colony stimulating factor (MCSF), soluble intercellular adhesion molecule-1 (sICAM), and soluble vascular cell adhesion molecule-1 with risk of COVID-19 outcomes. sICAM was positively associated with atopic asthma risk, whereas tumor necrosis factor-alfa showed an inverse association. A positive association was shown between interleukin-18 and COPD risk (replicated in observational analysis), whereas an inverse association was shown for interleukin-1 receptor antagonist (IL-1ra). IL-1ra and monocyte chemotactic protein-3 were positively associated with lung function indices, whereas inverse associations were shown for MCSF and interleukin-18 (replicated in observational analysis). Our results point to these cytokines as potential pharmacological targets for respiratory traits.


Data and data sharing
Provide the data used to perform all analyses or report where and how the data can be accessed, and reference these sources in the article.Provide the statistical code needed to reproduce the results in the article, or report whether the code is publicly accessible and if so, where

Cytokine instrument selection
Linkage disequilibrium (LD) structure was based on the European Population in the 1000 Genomes phase 3 reference panel.The genomic locations were identified using the University of California Santa Cruz (UCSC) Genome Browser (https://genome.ucsc.edu,human genome build 19, accessed on 18th June 2019).Gene expression data were obtained from the GTEx database, which provides a catalog of genetic variants that affect gene expression across multiple tissues, using data from 15,201 RNA-sequencing samples from 49 tissues of 838 post-mortem donors (version 8). [3]  Outcome

Colocalization
For each of the cytokine-outcome associations, we used the genomic region extending 50 kb on both sides of the lead cytokine variant.We used a Bayesian framework proposed by Giambartolomei, [4] that calculates posterior probabilities of several causal variant configurations, namely, no causal variant (H0), causal variant for exposure only (H1), causal variant for outcome only (H2), two distinct causal variants (H3) and a common causal variant (H4), under the assumption of a maximum of one causal variant for each trait, and we used a p12 prior (i.e.prior probability that a SNP is associated with both traits) threshold of 5×10 -04 .Results were considered as supportive of our MR results (i.e., with no evidence of genetic confounding) when PP of H4 was larger than 0.5, or alternatively, when the following two criteria were present: the sum of PP of H4 and PP of H3 was larger than 0.5, and PP of H4 was larger than PP of H3.This algorithm, described in a relevant publication, [5] was used due to low power.When evidence for colocalization was poor and there were multiple potentially causal variants (at least 2 independent instruments) we used the coloc Sum of Single Effects (SuSiE) framework, that relaxes the single causal variant assumption, to identify independent genetic signals and perform pair-wise colocalization analyses on all possible pairs of signals between the traits (the LD structure was accounted for using the European population of the 1000 Genomes phase 3 reference panel). [6] Observational analysis in the UK Biobank The associations that emerged in the MR analyses were explored in the UK biobank (UKBB, proposal ID: 79696). [7]The UKBB is an ongoing prospective cohort study which enrolled 502,412 participants aged 40 to 69 years from 22 assessment centers across the UK between 2006 and 2010. [8]At enrollment, participants provided signed consent, and answered questions on socio-demographic, lifestyle and health-related factors, and completed a range of physical measures.They also provided blood, urine and saliva samples, which were subsequently used to perform a range of assays, such as proteomic analyses.Blood plasma samples randomly selected from 54,306 UKBB participants were analyzed using the Olink Explore 1536 platform, capturing 1,463 unique proteins. [9]The following proteins codes were included in the observational analyses: 1392 -Interleukin-1 receptor antagonist protein (IL1ra), 1380 -Interleukin-18 (IL18), 401 -C-C motif chemokine 7 (MCP3), 696 -Macrophage colony-stimulating factor 1 (MCSF), 1314 -Intercellular adhesion molecule 1 (sICAM), 2847 -Vascular cell adhesion protein 1 (sVCAM), 2712 -Tumor necrosis factor (TNFa).Circulating plasma protein concentrations were inverse-rank normalized before modelling.

Figure S1 :
Figure S1: Iterative leave-one-out analysis for Mendelian randomisation analysis of genetically-proxied IL1RA and COPD, related to STAR methods

Figure S2 :
Figure S2: Iterative leave-one-out analysis for Mendelian randomisation analysis of genetically-proxied IL1RA and FEV1, related to STAR methods

Figure S3 .
Figure S3.Iterative leave-one-out analysis for Mendelian randomisation analysis of genetically-proxied IL18 and COPD, related to STAR methods

Figure S4 :
Figure S4: Iterative leave-one-out analysis for Mendelian randomisation analysis of genetically-proxied IL18 and FEV1, related to STAR methods

Figure S5 :
Figure S5: Iterative leave-one-out analysis for Mendelian randomisation analysis of genetically-proxied IL 18 and FVC, related to STAR methods

Figure S6 .
Figure S6.Iterative leave-one-out analysis for Mendelian randomisation analysis of genetically-proxied MCP3 and FEV1, related to STAR methods

Figure S7 :
Figure S7: Iterative leave-one-out analysis for Mendelian randomisation analysis of genetically-proxied MCP3 and FVC, related to STAR methods

Figure S15 :
Figure S15: Iterative leave-one-out analysis for Mendelian randomisation analysis of genetically-proxied TNF-a and atopic asthma, related to STAR methods

INTRODUCTION
Explain the scientific background and rationale for the reported study.What is the exposure?Is a potential causal relationship between exposure and outcome plausible?Justify why MR is a helpful method to address the study question

Study design and data sources Present
key elements of the study design early in the article.Consider including a table listing sources of data for all phases of the study.For each data source contributing to the analysis, describe the following: